Author

Louis Dijkstra

Centrum Wiskunde & InformaticaORCID 0000-0003-1476-6202Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Genomics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Research Focus:database / resource· 1 Research Focus:software / tool· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Tobias MarschalliD· 3 shared Alexander SchönhuthiD· 2 shared Johannes KösteriD· 2 shared Adam Novak· 1 shared Ali GhaffaariiD· 1 shared Bas E Dutilh· 1 shared Ben Langmead· 1 shared Benedict PateniD· 1 shared

Activity by Year

2016-2020 · peak 1/yr

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10 top collaborators · 31 mutual co-author links

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3 shared papersBioinformatics

Tobias MarschallORCID

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3 papers

Recent·Most cited

BioinformaticsGenome biologyPeer-reviewedApr 28, 2020

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

Johannes Köster, Louis Dijkstra, Tobias Marschall, Alexander Schönhuth

This paper introduces a statistical model to improve sensitivity and control false discovery rate in somatic indel discovery by accurately quantifying uncertainties.

32 citations

whole-exome sequencingstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintMar 29, 2017

Enhancing Sensitivity And Controlling False Discovery Rate In Somatic Indel Discovery Using A Latent Variable Model

Louis Dijkstra, Johannes Köster, Tobias Marschall, Alexander Schönhuth

This paper introduces a latent variable model to improve sensitivity and control false discovery rate in somatic indel discovery, showing significant improvements in recall for deletions.

DOI 10.1101/121954

whole-genome sequencingstatistical model

𝕏 PostBluesky

GenomicsBriefings in BioinformaticsPeer-reviewedOct 21, 2016

Computational Pan-Genomics: Status, Promises and Challenges

Tobias Marschall, Manja Marz, Thomas Abeel…·Pisan

This paper reviews computational pan-genomics, discussing its status, potential, and challenges across multiple biological disciplines.

DOI 10.1093/bib/bbw089

whole-genome sequencingstatistical model

𝕏 PostBluesky